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EvoNRL: Evolving Network Representation Learning Based on Random Walks

  • Farzaneh Heidari
  • Manos Papagelis
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

Large-scale network mining and analysis is key to revealing the underlying dynamics of networks. Lately, there has been a fast-growing interest in learning random walk-based low-dimensional continuous representations of networks. While these methods perform well, they can only operate on static networks. In this paper, we propose a random-walk based method for learning representations of evolving networks. The key idea of our approach is to maintain a set of random walks that are consistently valid with respect to the updated network topology. This way we are able to continuously learn a new mapping function from the new network to the existing low-dimension network representation. A thorough experimental evaluation is performed that demonstrates that our method is both accurate and fast, for a varying range of conditions.

Keywords

Network representation Evolving networks Random walks 

References

  1. 1.
    Ahmed, A., Shervashidze, N., Narayanamurthy, S., Josifovski, V., Smola, A.J.: Distributed large-scale natural graph factorization. ACM (2013)Google Scholar
  2. 2.
    Antoniak, M., Mimno, D.: Evaluating the stability of embedding-based word similarities. TACL 6, 107–119 (2018)Google Scholar
  3. 3.
    Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE TPAMI 35(8), 1798–1828 (2013)Google Scholar
  4. 4.
    Breitkreutz, B.J., et al.: The biogrid interaction database: 2008 update. Nucleic Acids Res. 36(suppl 1), D637–D640 (2007)Google Scholar
  5. 5.
    Cai, H., Zheng, V.W., Chang, K.: A comprehensive survey of graph embedding: problems, techniques and applications. IEEE TKDE (2018)Google Scholar
  6. 6.
    Goyal, P., Kamra, N., He, X., Liu, Y.: Dyngem: deep embedding method for dynamic graphs. In: IJCAI Workshop on Representation Learning for Graphs (2018)Google Scholar
  7. 7.
    Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: KDD, pp. 855–864 (2016)Google Scholar
  8. 8.
    Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. CoRR abs/1605.09096 (2016)
  9. 9.
    Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: methods and applications. IEEE Data Eng. Bull. 40(3), 52–74 (2017)Google Scholar
  10. 10.
    Jiang, M., Fu, A.W.C., Wong, R.C.W.: Reads: a random walk approach for efficient and accurate dynamic simrank. Proc. VLDB Endow. 10(9), 937–948 (2017)Google Scholar
  11. 11.
    Kim, Y., Chiu, Y., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. CoRR abs/1405.3515 (2014)
  12. 12.
    Li, J., Dani, H., Hu, X., Tang, J., Chang, Y., Liu, H.: Attributed network embedding for learning in a dynamic environment. In: CIKM, pp. 387–396 (2017)Google Scholar
  13. 13.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 3111–3119(2013)Google Scholar
  14. 14.
    Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)Google Scholar
  15. 15.
    Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. CoRR abs/1403.6652 (2014)
  16. 16.
    Reza, Z., Huan, L.: Social computing data repository. http://socialcomputing.asu.edu/
  17. 17.
    Trivedi, R., Dai, H., Wang, Y., Song, L.: Know-evolve: deep temporal reasoning for dynamic knowledge graphs. ICML 70, 3462–3471 (2017)Google Scholar
  18. 18.
    Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. KAIS 42(1), 181–213 (2015)Google Scholar
  19. 19.
    Zhang, D., Yin, J., Zhu, X., Zhang, C.: Network representation learning: a survey. IEEE Trans. Big Data (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.York UniversityTorontoCanada

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